2024; 22(2): 253-262  https://doi.org/10.9758/cpn.23.1070
Effect of Frailty on Depression among Patients with Late-life Depression: A Test of Anger, Anxiety, and Resilience as Mediators
Junhyung Kim1, Hyun-Ghang Jeong1, Moon-Soo Lee1,2, Chi-Un Pae3,4,5, Ashwin A. Patkar6, Sang Won Jeon7,8, Cheolmin Shin9, Changsu Han1
1Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
2Department of Life Sciences, Korea University, Seoul, Korea
3Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Korea
4Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
5Cell Death Disease Research Center, College of Medicine, The Catholic University of Korea, Seoul, Korea
6Department of Advance Psychiatry, Rush University Medical Center, Raleigh, NC, USA
7Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
8Workplace Mental Health Institute, Kangbuk Samsung Hospital, Seoul, Korea
9Department of Psychiatry, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
Correspondence to: Changsu Han
Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea
E-mail: hancs@korea.ac.kr
ORCID: https://orcid.org/0000-0002-4021-8907
Received: March 6, 2023; Revised: May 22, 2023; Accepted: May 25, 2023; Published online: September 6, 2023.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective: While the association between depression and frailty in the elderly population has been investigated, the psychological factors that mediate such a relationship remain unknown. The identification of psychological factors in interventions for depression treatment in the elderly may assist in the treatment and care. We aimed to explore the mediating effects of anger, anxiety, and resilience on the link between frailty and depression symptoms in patients with late-life depression.
Methods: A sample of 203 older adults completed questionnaires that assessed depression, anger, resilience, and anxiety. To measure frailty, participants were evaluated using a self-rated health questionnaire, weight-adjusted waist index related to sarcopenia, and weight-adjusted handgrip strength to evaluate weakness. A mediation model was tested, hypothesizing that anger, anxiety, and resilience would partially mediate the strength of the frailty-depression link in the elderly.
Results: Only self-rated health showed a significant association with depressive symptoms in late-life depression. Our study demonstrated that frailty has both direct and indirect associations with depression, mediated by anger, resilience, and anxiety.
Conclusion: Given that anger, resilience, and anxiety influence the link between self-rated health and depression, interventions that lead to increased resilience and decreased anger and anxiety may be promising to reduce depressive symptoms in older adults with depression.
Keywords: Anxiety; Anger; Depression; Aged; Hand-grip strength; Frailty

Major depressive disorder causes symptoms such as depressed mood, insomnia, and decreased appetite, increases the risk of suicide, and causes loss of social functioning [1]. Major depressive disorder occurring in the population over 60 years of age, classified as late-life depression (LLD), is the most common comorbidity and a risk factor associated with cognitive impairment, including dementia, and is known to exacerbate or induce other medical problems [2,3]. The prevalence of LLD increased by 27.1% worldwide from 2007 to 2017 [4]. Therefore, it is critical to understand the factors that influence the symptoms, occurrence, and course of LLD.

Frailty is a condition characterized by vulnerability to stressors associated with biological aging. It is defined as a loss of physiological reserve that contributes to functional decline, disability, disease state, and mortality [5,6]. Cli-nical parameters to determine frailty include accidental weight loss, weakness, poor endurance or weariness (low energy), slowness (slow walking), and limited physical activity [7]. Because frailty is common in elderly individuals [8,9] and the association between frailty and LLD has been consistently reported in various studies [10], there is a need for research on psychological factors between LLD and frailty to investigate effective interventions for LLD with respect to clinical similarities [11]. However, previous studies have only investigated the direct relationship between frailty and LLD [12,13], and not how these two factors interact.

Among the variables related to frailty, sarcopenia (cha-racterized by decreased muscle mass and strength) [14,15], and weakness (as measured by grip strength) were consistently found to influence age-related physical function loss [16,17] and medical conditions such as metabolic disorders and cardiovascular disorders [18,19]. Additionally, the subjective health state defined by an individual’s perceived health state can be utilized as a psychological domain related to frailty and is known to predict mortality due to disease [20,21]. It has also been reported that deteriorated subjective health status causes aggravation of depression and anxiety symptoms in the elderly without the onset or exacerbation of significant diseases, and is highly correlated with chronic diseases [22,23]. However, an investigation of the multiple domains related to frailty in LLD at a time is still lacking in the literature. To bridge this gap, it is necessary to apply various assessment tools, including the assessment of physical and psychological domains closely related to frailty.

Because of the importance of hypotheses for etiopathogenesis, including age-related factors in LLD [24], understanding the psychological factors in the interaction between frailty and LLD is essential in developing a treatment program for patients. According to the cognitive-behavioral model for depression, a negative bias associated with stressors may lead an individual to depression [25]. Therefore, along with frailty, it can be inferred that some cognitive appraisal variables may open the possibility to investigate how to alter the relationship between frailty and depression symptoms in elderly individuals. Although there has been no mediation study on frailty and LLD, to our knowledge, accumulating evidence has demonstrated that anger [26,27], anxiety [28,29], and resilience are associated with both frailty and depression in elderly individuals [30,31]. Given the direct relationship between frailty and LLD, these findings suggest that anger, anxiety, and resilience might mediate the relationship between frailty and LLD.

Although the detrimental influence of frailty on LLD has been studied, the mechanisms underlying the association between frailty and LLD are yet to be clarified. To fully appreciate these links, we believe it is crucial to identify the roles of anger, resilience, and anxiety in the relationship between depression and frailty. The hypothesized model evaluated three pathways: (i) connection between frailty and depression through anger; (ii) connection between frailty and depression through resilience; and (iii) connection between frailty and depression through anxiety. The model also evaluates the direct effect of frailty on depression by adjusting for all mediators included in the model. We used both the physical and psychological domains of frailty to examine the impact of each.


Study Design and Participants

We conducted a cross-sectional study of patients who visited our psychiatric clinic, hospital, or dementia relief center, which conducted a national screening for dementia, from April 2021 to February 2022. Participants were recruited via announcements posted at each site. Two psychiatrists interviewed candidates about their medical and surgical histories for screening. The subjects were included if (i) their age was between 60−90 years; (ii) they were diagnosed with major depressive disorder according to the diagnostic and statistical manual of mental disorder version 5 (DSM-V) through an interview with a mental health specialist; and (iii) they were capable of communicating in Korean and were willing to participate in the interview. Participants were excluded if: (i) they suffered from cognitive impairment, including mental retardation and dementia; (ii) had a history of psychiatric disorders, such as schizophrenia spectrum disorder, bipolar affective disorder, and substance or alcohol dependence or abuse; (iii) were diagnosed with Parkinson’s disease; (iv) had a history of organic nervous system disease or trauma, such as stroke or cerebral hemorrhage; and (v) had changed medication for medical or surgical disease within six months from the date of written consent.

In total, 205 individuals satisfied the eligibility criteria and participated in the study. Two participants withdrew their consent to participate during the assessments. Body weight, height, waist circumference, and grip strength were measured by one investigator. In-person interviews were conducted to compile a set of questionnaires. Before the survey, a training program for the investigator was executed to limit the impact of variability on data collection.

Ethical Considerations

The Ethics Committee of the Korea University Guro Hospital approved this study (IRB number: 2021GR0122). All participants provided informed consent for participation. They were informed that their privacy would be safeguarded, and that the data collected would be utilized exclusively for the current study. The complete privacy of the data was guaranteed.

Dependent Variable: Depression

Geriatric depression scale

The geriatric depression scale (GDS) is a 15-item questionnaire often used to evaluate depression in elderly individuals. Participants answered questions about their emotions during the previous week. When they responded ‘no’ to items 1, 5, 7, 11, and 13 and ‘yes’ to the remaining questions, depression was indicated. For each item, a point was given if the response suggested depression. The GDS has a score range of 0−15, with higher scores suggesting greater depressive symptoms. A score of over five suggests clinically significant depressive symptoms. Using our data, we estimated that the Cronbach’s alpha was 0.81.

Independent Variable: Frailty

Self-rated health inventory

The self-rated health (SRH) questionnaire comprises four single items with changes in question framing, one of which asks respondents to assess their health on an overall scale, while the other three questions focus on their health from the perspective of their particular aging process. These questions were based on a previous study by Svedberg et al. [32], which was a longitudinal investigation of self-rated health. The general health item read ‘How would you rate your general health status?’ For this item, the response options were: 1 = ‘bad’; 2 = ‘reasonable’; and 3 = ‘good.’ There were two comparative items, ‘How would you rate your general health status compared to 5 years ago?’ and the social-comparative item ‘How would you rate your health status compared to others in your age group?’ For these two items, the response options were: 1 = ‘worse’; 2 = ‘about the same’; 3 = ‘better.’ The final item read ‘Do you think your health prevents you from doing things you would like to do?’ The response options for this item were: 1 = ‘to a great extent’; 2 = ‘partly’; and 3 = ‘not at all.’ Since this study was a cross-sectional study and not a time series analysis like Svedberg’s study, the total score was calculated by summing the scores without a standardization process. A higher score indicated a more positive perception of one’s own health. The items were homogeneous (Cronbach’s α = 0.689).

Weight-adjusted waist index

The weight-adjusted waist index (WWI) was used as a straightforward and reliable assessment for sarcopenia. This index represents the antagonistic effect of fat and muscle mass in older adults [33]. Height and bodyweight were assessed to the closest 0.1 cm and 0.1 kg, respectively. Waist circumference was measured at the midpoint between the iliac crest and lower rib border in the standing position. The WWI (cm/√kg) was computed by dividing the waist circumstance (cm) by the square root of weight (kg) [34].

Weight-adjusted grip strength

A digitized grip strength dynamometer (TKK 5401; Takei Scientific Instruments Co. Ltd.) was used to assess handgrip strength as a measure of weakness. We evaluated the grip strength of the dominant hand in a standardized manner [13]. The staff directed the participants to remove jewelry from their fingers and wrists and provided a comprehensive explanation of the measuring technique and process. Next, the inner lever of the dynamometer was fitted to the hand with the elbow at a 90° angle, the wrist in a neutral position (while maintaining the upper arm tight against the trunk), and the elbow at a 90° angle. Participants gripped the instrument as tightly as possible for 3 seconds, three times with each hand. A rest period of at least 60 seconds was provided between each measurement. The highest handgrip strength score of the dominant hand was used in the analysis. The weight-adjusted grip strength (WGS) was calculated as the handgrip strength score divided by the square root of the weight (kg) [13].

Mediating Variable

State-trait anger expression inventory: state anger

There are two types of anger: state and trait. State anger (SA) is a transient emotional state related to an anger-provoking situation [35]. Spielberger [35] developed the State and Trait Anger Inventory to evaluate anger-related factors and anger expression characteristics; Chon et al. [36] standardized it for Koreans. This scale consists of 44 items, including 10 for SA, 10 for trait anger, and 24 for anger expressions. Because an SA that changes over time will better reflect the response to provocation [35], in the current study, the 10 items of the SA scale were rated on a four-point Likert scale (score range: 10−40), with 1-point indicating ‘not true at all’; 2 points, ‘somewhat true’; 3 points, ‘true’, and 4 points, ‘very true’. Larger degrees of perceived anger correspond to higher levels of SA. The tool had a Cronbach’s α of 0.95 for SA [36].

Generalized anxiety disorder-7

Generalized anxiety disorder-7 (GAD-7) is a seven-item self-report assessment created to screen for GAD [37]. Participants were asked how frequently they had been plagued by anxiety symptoms (such as difficulties relaxing or excessive worry about various topics) over the last two weeks using a 4-point Likert-type scale (0 = not at all; 3 = nearly every day). The higher the score, the more severe the GAD symptom. GAD-7 has been proven to have sufficient psychometric qualities, such as strong internal consistency, clinical value, and construct validity. This study used the Korean version of the GAD-7, which is accessible on the Patient Health Questionnaire website (https://www.phqscreeners.com).

Brief resilience scale

The brief resilience scale (BRS) is a self-report measure of resilience that contains six items [38]. Three of them (items 1, 3, and 5) are positive, and the other three (2, 4, and 6) are negative. Each item is rated on a scale from 1 to 5, and the scores for the negative questions are summed in reverse. Higher scores indicate higher resilience and are negatively correlated with anxiety and depression. We used the Korean version of the BRS validated by Kim et al. [39]. In this study, the BRS showed good internal consistency, with a Cronbach’s alpha of 0.791.

Covariate Variable

Individual-level variables, including age (years), sex (male and female), years of education, and number of chronic diseases (less than two and two or more), were controlled to reduce omitted variable bias. Psychiatrists collected individual-level information during the interviews.

Statistical Analyses

Spearman’s correlation analyses of the dependent (GDS), independent (frailty: SRH, WWI, and WGS), and mediating variables (SA, BRS, and GAD) were performed using IBM SPSS Statistics version 28.0 (IBM Corp.). Mplus 7.4 (Muthén and Muthén) software was used to compute the mediation metrics for the total, direct, and indirect effects, employing a mediation model with independent variables for frailty that showed a significant correlation with the GDS as a predictor, three potential mediators (SA, BRS, and GAD), and GDS as the outcome. Bootstrapping, a non-parametric resampling method, was used to assess mediation without distributional assumptions because it is more robust than either Sobel’s test or the Baron and Kenny methodology. Bootstrapping can be used to draw conclusions regarding indirect effects in models containing intervening variables, despite the complexity and variety of paths between the independent and dependent variables [40]. Before employing bootstrapping, we assessed the goodness of fit of our proposed mediation model using the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). An acceptable model fit was determined based on the following cutoff criteria: CFI and TLI > 0.90, RMSEA < 0.08, and SRMR < 0.08. The significance of the direct and indirect effects was evaluated by bias correction using a confidence range of 95%. Age, sex, years of education, and number of chronic diseases were considered as statistical covariates. Two-tailed tests were used for all statistical analyses.


Sample Characteristics

Table 1 presents the characteristics of the participants and a detailed overview of the sex differences. Of the 203 participants, 140 were women and 63 were men. The mean age was 74.72 years (95% confidence interval [CI], 73.82−75.63). In total, 66.01% of the participants had less than two chronic diseases. Mean education years was 8.28 years (95% CI, 7.39−9.17).


Table 2 presents the Spearman’s rank correlation analyses. Among the independent variables related to frailty, only SRH was significantly correlated with GDS (r = −0.577, p < 0.01). Depression was negatively correlated with resilience (r = −0.332, p < 0.01) and positively correlated with anger (r = 0.444, p < 0.001) and anxiety (r = 0.540, p < 0.001). SRH was significantly correlated with anger (r = −0.364, p < 0.001), resilience (r = 0.281, p < 0.001), and anxiety (r = −0.0369, p < 0.001).

Mediation Analysis

To test the hypothesized model, the effects of SRH on depressive symptoms were examined, with anger, resilience, and anxiety as potential mediating variables. Regarding the fit, statistics of the mediation model using anger, resilience, and anxiety were well within the limits that allowed the model to be accepted; anger (CFI = 1.000, TLI = 1.000, RMSEA < 0.001, and SRMR < 0.001), resilience (CFI = 1.000, TLI = 1.000, RMSEA < 0.001, and SRMR < 0.001), and anxiety (CFI = 1.000, TLI = 1.000, RMSEA < 0.001, and SRMR < 0.001). Table 3 shows that SRH was negatively associated with depression via three indirect pathways: (i) anger (indirect effect = −0.235, p < 0.001); (ii) resilience (indirect effect = −0.130, p < 0.001); and (iii) anxiety (indirect effect = −0.427, p < 0.001). Figure 1 shows the indirect association of frailty and depression with anger, resilience, and anxiety. The mediation analysis indicated that better SRH was associated with lower levels of anger and anxiety and higher levels of resilience. These factors, in turn, appeared to have a serial effect on depression. Our findings suggest that the relationship between SRH and depression may be mediated by the combined influence of anger, resilience, and anxiety. The three models concerning anger, resilience, and anxiety accounted for 11.6%, 10.9%, and 17.5% of the variance in depression, respectively.


In accordance with our hypothesis, the mediation analysis showed that the influence of the psychological domain of frailty on depressive symptoms is partly mediated by anxiety, resilience, and anger. Only SRH showed a significant association with depressive symptoms in LLD. Frailty has both direct and indirect associations with depression, mediated by anger, resilience, and anxiety.

Anger acted as a mediator between SRH and depression. Anger is often defined as a negative emotional reaction to perceived provocation [41]. Further, frailty-related factors, such as decreased function and deterioration of health status, may increase negative emotions, such as apathy or anger expression [26]; indeed, SRH can provoke anger [42], and increased anger exacerbates depression [27,43,44].

A recent study of anger as a risk factor for depression in COVID-19 patients supports the findings of this study that health-related factors may influence anger-related depression [45]. The results of our study provide a basis for the importance of looking into anger among the emotional reactions to frailty in elderly depression because modifying anger is vital for both the management of depression and health-promoting behavior in the elderly [46,47].

Our study also revealed that anxiety affects the relationship between SRH and depression among elderly individuals with depression. Further, it is difficult to realize that anxiety unilaterally affects depression because anxiety and depressive disorders are highly comorbidly prevalent disorders in the elderly [48]. A previous study suggested that individuals with frailty show a higher likelihood of both depressive and anxiety symptoms [28]; thus, it can be inferred that anxiety and depression should both be monitored in elderly individuals with frailty. Moreover, anxiety in older adults is shown relatively little interest and is less frequently referred for specialized mental health care than mood disorders in older adults is [49]. Taking appropriate measures to manage anxiety in old age could prove to be appropriate and cost-effective. A cost-effective way to help individuals with anxiety is to pay special attention to the elderly at risk of developing anxiety [50]. Therefore, our study provides a basis for paying special attention to frailty-related anxiety in patients with LLD.

Resilience may partly mediate the association between poor SRH and depressive symptoms. Poor SRH may predict lower resilience, as suggested in a previous study [31], and decreased resilience is correlated with higher depressive symptoms [51]. Moreover, more resilient individuals have better clinical and functional outcomes, which may be used as goals for preventive or therapeutic interventions [30]. In addition, a previous study highlighted the need of adopting interventions to promote resilience in the elderly to prevent and manage depressive symptoms [30]. Additionally, early interventions to foster resilience provide protection from depression [50]. In this context, the role of improving resilience in the design and implementation of interventions needs to be emphasized, and an understanding needs to be developed that frailty should be addressed among elderly individuals with frailty and depression.

WGS and WWI, as measured by physical domain measurements related to frailty, did not correlate significantly with GDS in the current study which is in contrast to the results of a previous study that reported significant positive correlations [12]. Differences in the characteristics of the study populations are thought to be a major contributor to these differences in results. Whereas the previous study was conducted among the general population [12], our study recruited older adults with a certain level of depression. Therefore, our results should be interpreted in terms of a specific group, namely individuals with LLD. Additionally, while the previous studies examining the relationship between frailty and depression used only a single measurement tool for frailty [12,13], the current study measured multiple domains of frailty. Based on the definition of frailty suggested by Fried et al. [7], which proposes a reliable and comprehensive assessment of frailty [51,52], and on our results introducing multiple domains of frailty, we speculate that the impact of the physical domain of frailty is less relevant for clinical groups, such as groups comprising patients with LLD. Moreover, since the depression cognitive-behavior model suggested that the perception of the actual situation has an essential effect on the symptoms, this result suggests that the psychological domain of frailty is more significantly related to the severity of symptoms in patients with depression than the physical domain of frailty [53]. Therefore, it is necessary to focus on the psychological domain of frailty in patients with LLD. As some aspects of the definition suggested by Fried et al. [7]. are pertinent and difficult to apply in clinical practice [54], the higher relevance of the psychological domain observed in our study has clinical value.

To our knowledge, no previous study has explored the association between frailty and depression using a mediation model of anger, resilience, and anxiety among the elderly. Our research demonstrated that frailty has direct and indirect links with depression mediated by anger, resilience, and anxiety. These findings imply that greater attention should be given to seniors with depression and frailty, who are prone to progress to more severe depression, than to those with high levels of anger, anxiety, or resilience. Given that anger, resilience, and anxiety influence the link between SRH and depression, interventions that lead to increased resilience and reduced anger and anxiety may effectively reduce depressive symptoms in older adults. Several therapies, such as cognitive-behavioral therapy, which focus on transforming a person’s negative thoughts into a realistic evaluation, should be investigated to ameliorate anger and anxiety. Meanwhile, gerontological practice should offer the evaluation and appropriate treatment of SRH to decrease depression.

Our study has several limitations. First, convenience sampling was chosen as the first sample technique due to the restricted budget and quantity of interviewers. These issues hampered the national population representation and external validity of the study. Second, due to the limitations of the cross-sectional design, the findings of mediation studies should be regarded with care in terms of their causal pathways. Furthermore, to demonstrate the predictive potential of the mediation model, longitudinal data should be gathered in future studies. Finally, anxiety, anger, and resilience are heterogeneous constructs, and further research on the interactions between them is needed for practical clinical applications. In particular, it would be interesting to evaluate how resilience mediates the effects of frailty on anxiety and anger. Therefore, future studies aimed at improving frailty-associated depression in older adults should consider these structural causal relationships in addition to longitudinal designs.

The current study revealed that the association between frailty and depression can be mediated by anger, resilience, and anxiety, which may suggest how frailty affects depressive symptoms from the standpoint of psychology. Therefore, based on this study, it is necessary to develop an intervention for older adults with frailty for anger, anxiety, and resilience and to conduct research on additional factors involved in the relationship between frailty and depression in the future. The strengths of our study include both objective measures and subjective factors. Our study demonstrated the importance of including subjective factors when evaluating the effect of frailty on LLD.


This work was supported by the Yungjin Pharm. Co., Ltd. (grant number YJP202101). The funders played no role in the design, conduct, or reporting of this study.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: Changsu Han, Junhyung Kim. Data acquisition: Changsu Han, Junhyung Kim, Hyun-Ghang Jeong, Moon-Soo Lee. Formal analysis: Junhyung Kim. Funding acquisition: Changsu Han. Writing—original draft: Junhyung Kim. Writing—review & editing: Changsu Han, Chi-Un Pae, Ashwin A. Patkar, Sang Won Jeon, Cheolmin Shin. Supervision: Changsu Han.

Fig. 1. Mediation models concerning self-rated health on depression in elders through potential mediators.
SRH, self-rated health inventory; GDS, geriatric depression scale; X, self-rated health inventory; M1, state anger expression inventory-anger state; M2, brief resilience scale; M3, generalized anxiety disorder-7; Y, geriatric depression scale.
Values represent the coefficient of unstandardized regression (bootstrap standard error), p > 0.05. *p < 0.05, **p < 0.001.

Sociodemographic and psychological characteristics of participants

Variable Value (n = 203)
Age 74.72 ± 6.54
Chronic disease
< 2 134 (66.01)
≥ 2 69 (33.99)
Education level (yr) 8.28 ± 6.43
Body mass index (kg/m2) 24.68 ± 2.97
Weight-adjusted waist index 11.09 ± 3.24
Weight-adjusted grip strength 2.64 ± 0.77
Self-rated health inventory 7.40 ± 2.18
Psychological characteristics
State anger expression inventory 11.02 ± 2.88
Brief resilience scale 18.37 ± 5.28
Geriatric depression scale 13.64 ± 6.32
Generalized anxiety disorder-7 3.00 ± 4.43

Values are presented as mean ± standard deviation or number (%).

Correlation between frailty and psychological characteristics (n = 203)

WWI 0.048 -
WGS −0.100 −0.321** -
SRH −0.531** 0.066 0.096 -
SA 0.444** −0.064 −0.049 −0.302** -
BRS −0.332** −0.040 0.086 0.254** −0.257** -
GAD-7 0.540** −0.057 −0.063 −0.349** 0.006 −0.374** -

GDS, geriatric depression scale; WWI, weight-adjusted waist index; WGS, weighted-adjusted grip strength; SRH, self-rated health inventory; SA, state anger expression inventory-state anger; BRS, brief resilience scale; GAD-7, generalized anxiety disorder-7.

*p < 0.05, **p < 0.001.

Mediation analysis concerning the association between self-rated health and depression in the elderly

Point estimate Estimate Product of coefficients Bootstrapping (5,000)

Percentile 95% CI Bias corrected percentile 95% CI

SE Z Lower Upper Lower Upper
Total −1.498** 0.148 −10.097 −1.788 −1.207 −1.788 −1.207
X →M1 →Y −1.263** 0.150 −8.431 −1.545 −0.968 −1.554 −0.973
X →M2 →Y −1.368** 0.150 −9.137 −1.654 −1.069 −1.654 −1.069
X →M3 →Y −1.071** 0.151 −7.114 −1.361 −0.773 −1.366 −0.776
X →M1 →Y −0.235** 0.062 −3.766 −0.376 −0.129 −0.373 −0.127
X →M2 →Y −0.130** 0.059 −2.195 −0.262 −0.035 −0.279 −0.040
X →M3 →Y −0.427** 0.091 −4.697 −0.621 −0.268 −0.616 −0.266

All the values were unstandardized.

X, self-rated health inventory; M1, state anger expression inventory-anger state; M2, brief resilience scale; M3, generalized anxiety disorder-7; Y, depression; CI, confidence interval; SE, standard error; Z, z-value.

**p < 0.001.

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